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- W4324137384 abstract "SQL Injection Attacks are a concern to databasedriven online applications because they can compromise the security and integrity of information in databases. It gives hackers the potential to read, delete, modify or copy data. Exploiting the flaws in the validation of user inputs provided through login pages is the simplest technique to conduct this attack. Each login page a user accesses can help to reveal more about who they are. Feedbacks sent by the server while running a SQL command can provide details on weaknesses in the database server’s validation procedure. This research paper analyses code that focuses on detecting SQL injections. Different supervised Machine Learning algorithms are used to evaluate the model. A CNN model is also created for real-time identification of whether an input is a potential attack or not." @default.
- W4324137384 created "2023-03-15" @default.
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- W4324137384 date "2023-01-23" @default.
- W4324137384 modified "2023-09-27" @default.
- W4324137384 title "SQL Injection Detection using Machine Learning and Convolutional Neural Networks" @default.
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- W4324137384 doi "https://doi.org/10.1109/icssit55814.2023.10061019" @default.
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